Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment

Fu, Jun; Hou, Chen; Zhou, Wei; Xu, Jiahua; Chen, Zhibo

doi:10.1145/3503161.3548337

Cited by 19 publications

(6 citation statements)

References 23 publications

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“…HGNN extended the spectral method of graph convolutional neural network [32] to hypergraph convolution. As the field of hypergraph theory has advanced, more hypergraph neural networks have been further proposed, such as hypergraph convolutional neural networks [33], hypergraph recurrent neural networks [34], and hypergraph generative adversarial networks [35], which have demonstrated superior effectiveness in various fields, including recommendation engines [36,37], computer vision [38,39], bioinformatics [40,41]. HyperGCN-WSS [42] was developed to overcome the obstacle of requiring densely annotated images for semantic segmentation.…”

Section: Hypergraph Learningmentioning

confidence: 99%

HGSNet: A hypergraph network for subtle lesions segmentation in medical imaging

Wang,

Zhang,

et al. 2024

IET Image Processing

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Lesion segmentation is a fundamental task in medical image processing, often facing the challenge of subtle lesions. It is important to detect these lesions, even though they can be difficult to identify. Convolutional neural networks, an effective method in medical image processing, often ignore the relationship between lesions, leading to topological errors during training. To tackle topological errors, move is made from pixel‐level to hypergraph representations. Hypergraphs can model lesions as vertices connected by hyperedges, capturing the topology between lesions. This paper introduces a novel dynamic hypergraph learning strategy called DHLS. DHLS allows for the dynamic construction of hypergraphs contingent upon input vertex variations. A hypergraph global‐aware segmentation network, termed HGSNet, is further proposed. HGSNet can capture the key high‐order structure information, which is able to enhance global topology expression. Additionally, a composite loss function is introduced. The function emphasizes the global aspect and the boundary of segmentation regions. The experimental setup compared HGSNet with other advanced models on medical image datasets from various organs. The results demonstrate that HGSNet outperforms other models and achieves state‐of‐the‐art performance on three public datasets.

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Section: Hypergraph Learningmentioning

confidence: 99%

HGSNet: A hypergraph network for subtle lesions segmentation in medical imaging

Wang,

Zhang,

et al. 2024

IET Image Processing

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“…This makes the model significantly complex. Building on the VGCN architecture, Fu et al [29] proposed a similar solution that models interactions among viewports using a hyper-GNN [30].…”

Section: Multichannel Modelsmentioning

confidence: 99%

PW-360IQA: Perceptually-Weighted Multichannel CNN for Blind 360-Degree Image Quality Assessment

Sendjasni

Larabi

2023

Sensors

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Image quality assessment of 360-degree images is still in its early stages, especially when it comes to solutions that rely on machine learning. There are many challenges to be addressed related to training strategies and model architecture. In this paper, we propose a perceptually weighted multichannel convolutional neural network (CNN) using a weight-sharing strategy for 360-degree IQA (PW-360IQA). Our approach involves extracting visually important viewports based on several visual scan-path predictions, which are then fed to a multichannel CNN using DenseNet-121 as the backbone. In addition, we account for users’ exploration behavior and human visual system (HVS) properties by using information regarding visual trajectory and distortion probability maps. The inter-observer variability is integrated by leveraging different visual scan-paths to enrich the training data. PW-360IQA is designed to learn the local quality of each viewport and its contribution to the overall quality. We validate our model on two publicly available datasets, CVIQ and OIQA, and demonstrate that it performs robustly. Furthermore, the adopted strategy considerably decreases the complexity when compared to the state-of-the-art, enabling the model to attain comparable, if not better, results while requiring less computational complexity.

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“…This makes the model significantly complex. Inspired by the VGCN architecture, Fu et al [35] proposed a similar architecture, where the interaction among viewports is modeled using hyper-GNN [36]. Miaomiao et al [26] integrated saliency prediction in the design of a CNN model combining SP-NET [37] for saliency features' extraction, and ResNet-50 [30] for visual features' extraction.…”

Section: Related Workmentioning

confidence: 99%

“…The selected models include PSNR, SSIM, MS-SSIM [62], FSIM [63], BRISQUE [64], and BMPRI [65], DB-CNN [34], and DipIQ [66], representing 2D-IQA models. S-PSNR [6], WS-PSNR [6], SSP-BOIQA [11], Yun et al [12], MC360IQA [20], Zhou et al [23], VGCN [25], AHGCN [35], and S 3 DAVS [16], representing 360-IQA models. MC360IQA, Zhou et al, VGCN, and AHGCN are all deep learning-based solutions using the multichannel paradigm with a varying number of channels, from six to twenty, making them highly complex models.…”

Section: Performance Comparison With Sota Modelsmentioning

confidence: 99%

Attention-Aware Patch-Based CNN for Blind 360-Degree Image Quality Assessment

Sendjasni,

Larabi

2023

Sensors

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An attention-aware patch-based deep-learning model for a blind 360-degree image quality assessment (360-IQA) is introduced in this paper. It employs spatial attention mechanisms to focus on spatially significant features, in addition to short skip connections to align them. A long skip connection is adopted to allow features from the earliest layers to be used at the final level. Patches are properly sampled on the sphere to correspond to the viewports displayed to the user using head-mounted displays. The sampling incorporates the relevance of patches by considering (i) the exploration behavior and (ii) a latitude-based selection. An adaptive strategy is applied to improve the pooling of local patch qualities to global image quality. This includes an outlier score rejection step relying on the standard deviation of the obtained scores to consider the agreement, as well as a saliency to weigh them based on their visual significance. Experiments on available 360-IQA databases show that our model outperforms the state of the art in terms of accuracy and generalization ability. This is valid for general deep-learning-based models, multichannel models, and natural scene statistic-based models. Furthermore, when compared to multichannel models, the computational complexity is significantly reduced. Finally, an extensive ablation study gives insights into the efficacy of each component of the proposed model.

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Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment

Cited by 19 publications

References 23 publications

HGSNet: A hypergraph network for subtle lesions segmentation in medical imaging

HGSNet: A hypergraph network for subtle lesions segmentation in medical imaging

PW-360IQA: Perceptually-Weighted Multichannel CNN for Blind 360-Degree Image Quality Assessment

Attention-Aware Patch-Based CNN for Blind 360-Degree Image Quality Assessment

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